Automated dementia detection from spontaneous speech using disfluency-augmented nlp and discourse graph analysis
TL;DR
The Pause-Augmented for Disfluency Markers framework was developed, a novel preprocessing approach that explicitly encodes speech disfluencies as special tokens, preserving them for consumption by the classifiers.
Abstract
Recent advances in Natural Language Processing (NLP) have enabled new approaches for assessing cognitive health through speech analysis. Dementia remains a growing public health concern, yet diagnosis often relies on costly and time-consuming clinical assessments. Speechbased screening offers an alternative, as language deterioration is among the earliest signs of cognitive decline. However, most existing methods fail to effectively capture both how something is said (from the audio) and what is said (from the transcribed text). Yet both components are critical for accurate and reliable diagnosis. Therefore, this work explores independent approaches to evaluate the best-performing methods. Through systematic experimentation, it was found that standard NLP preprocessing pipelines remove clinically relevant disfluency patterns from speech transcripts before modelling, discarding features that may distinguish dementia from healthy speech. To address this, the Pause-Augmented for Disfluency Markers framework was developed, a novel preprocessing approach that explicitly encodes speech disfluencies as special tokens, preserving them for consumption by the classifiers.